/Machine-Learning-Coursera

Primary LanguageMATLAB

Machine-Learning-Coursera

This is dervied from the machine learning course in Coursera by Andrew Ng.
There are total 8 exercises in each chapter as follows.

ex1 (Linear Regression)

  1. Warm up exercise
  2. Compute cost for one variable
  3. Gradient descent for one variable
  4. Feature Normalization
  5. Compute cost for multiple variables
  6. Gradient descent for multiple variables
  7. Normal equations

ex2 (Logistic Regression)

  1. Sigmoid Function
  2. Compute cost for logistic regression
  3. Gradient for logistic regression
  4. Predict function
  5. Compute cost for regularized LR
  6. Gradient for regularized LR

ex3 (Multi-class Classification and Neural Networks)

  1. Regularized logistic regression
  2. One-vs-all classifier training
  3. One-vs-all classifier prediction
  4. Neural network prediction function

ex4 (Neural Network Learning)

  1. Feedforward and cost function
  2. Regularized cost function
  3. Sigmoid gradient
  4. Neural net gradient function (backpropagation)

ex5 (Machine Learning System Design)

  1. Regularized linear regression cost function
  2. Regularized linear regression gradient
  3. Learning Curve
  4. Polynomial feature mapping
  5. Cross validation curve

ex6 (Support Vector Machines)

  1. Gaussian kernel
  2. Parameters(C, sigma) for dataset 3
  3. Email preprocessing
  4. Email feature extraction

ex7 (K-Means Clustering and PCA)

  1. Find closest centroids
  2. Compute centroid means
  3. PCA
  4. Project data
  5. Recover data

ex8 (Anomaly Detection and Recommender Systems)

  1. Estimate gaussian parameters
  2. Select threshold
  3. Collaborative filtering cost
  4. Collaborative filtering gradient
  5. Regularized cost
  6. Gradient with regularization